Prospective, blind study of the triple stimulation technique in the diagnosis of ALS

Abstract Objective: To evaluate the diagnostic yield of magnetic cortical stimulation with the triple stimulation technique (TST) to identify upper motor neuron (UMN) involvement in patients suspected of having ALS. Methods: Fifty-nine patients were recruited to undergo TST in addition to the standard work-up for suspected motor neuron disease. TST combines transcranial magnetic stimulation of the motor cortex with collision studies, which results in a higher sensitivity in detecting UMN involvement. Primary outcome was the number of abnormal TST results in patients with possible ALS. The positivity rate was converted to the number needed to test with TST (NN-TST) for one extra diagnosis of ALS. Results: Fifty patients underwent TST. In the total group (n 059), 18 patients had a motor neuron disorder but did not fulfil criteria for 'probable' or 'definite' ALS. In four of these patients TST was abnormal (NN-TST, 4.5). One TST was erroneously interpreted as abnormal. TST findings were normal in inclusion body myositis and peripheral nerve disorders. Conclusion: This prospective and blind study confirms open studies of TST in the evaluation of ALS. We suggest that TST can be used to arrive at a diagnosis of 'probable' or 'definite' ALS in patients lacking UMN signs in the upper extremities

Verdienmodellen natuurinclusieve landbouw

Deze brochure richt zich op agrarische ondernemers en hun adviseurs. Wilt u nieuwe ideeën opdoen voor verdienmodellen gericht op de combinatie van landbouw met natuur? Wilt u voorbeelden van wat natuur voor u kan betekenen en hoe u hierin stappen kunt zetten? Laat u dan inspireren door deze brochure. En ga samen met verschillende partijen om de tafel om een natuurinclusief verdienmodel samen te stellen

Cereal yield gaps across Europe

peer-reviewedEurope accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe’s production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe’s potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha−1 for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe’s role in global food security, farm economic objectives and environmental targets.We received financial contributions from the strategic investment funds (IPOP) of Wageningen University & Research, Bill & Melinda Gates Foundation, MACSUR under EU FACCE-JPI which was funded through several national contributions, and TempAg (http://tempag.net/)

Maximising meaning: creating a learning environment for reading comprehension of informative texts from a Vygotskian perspective

Sociocultural theories based on the work of Vygotsky have been increasingly influential in educational sciences. Developmental education (DE) is a pedagogical approach based on Vygotskian theory that has inspired primary schools in the Netherlands to change the learning environment innovatively in a comprehensive way. In this article, we focus on the learning environment for reading comprehension of informative texts in upper-primary grade classrooms in DE. Our aim is to contribute to a more profound understanding of the characteristics of learning environments that are inspired by a Vygotskian approach and that are conducive to reading comprehension of informative texts. Five fourth-grade expert DE teachers participated in a multiple case study aimed at describing and analysing these characteristics for the domain of reading informative texts. Data were collected over a period of six to eight weeks for each teacher and consisted of videotaped interviews, classroom observations and documents. We conclude that DE learning environments are focused on maximising meaning from text for students. This is achieved by organising learning on the basis of emergent goals within students’ participation in sociocultural practices

UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture

To explore the diurnal variations, radiometric and geometric accuracy of UAV-based data for precision agriculture, a comprehensive dataset was created in a one-day field campaign (21 June 2017). The multi-sensor data set covers wheat, barley & potato experimental fields, located in Wageningen University and Research (WUR) farm maintained by Unifarm. UAV-based images were collected with several sensors over the experimental area, starting from 7:25am and ending at 20:00pm local solar time. The dataset consists of images collected by 9 flights with senseFly MSP4C, 9 with Parrot Sequoia, 2 with Slant Range P3, 5 with DJI Zenmuse X3 NIR, 4 with the senseFly Thermo-map and 1 with the RGB Sony WX-220. Additionally, validation measurements at radiometric calibration plates and plant sample locations were taken with a Cropscan handheld spectrometer and a tec5 Handyspec spectrometer. The dataset consists of the validation measurements, the raw images and the processed orthomosaics (both with and without geometric correction)

Reading for meaning: the effects of Developmental Education on reading achievements of primary school students from low SES and ethnic minority families

The appropriateness of innovative educational concepts for students from a low socioeconomic status (SES) or ethnic minority background is sometimes called into question. Disadvantaged students are supposed to benefit more from traditional approaches with Programmatic Instruction (PI). We examined Developmental Education (DE), an innovative approach, inspired by Vygotskian theory, in which reading skills are developed through meaningful reading of texts corresponding to students’ self-generated problems. The effectiveness of DE is compared to PI in terms of reading comprehension, strategy knowledge, and reading motivation of 4th-grade students; 170 students from ethnic minority or low SES background participated in a pretest-posttest natural 2-group design. Outcomes were similar in both approaches, with one exception: Students with an ethnic minority background in DE performed better on strategy knowledge than similar students in PI. These results are discussed in relation to previous studies on the appropriateness of innovative curricula for disadvantaged students

Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery : A triennial study in an apple orchard

A timely and accurate spatial inventory of flowering characteristics benefits both the floral phenology monitoring in ecology and various crop management activities in agricultural systems. Recent advancement has proven the superiority of computer vision in flower classification at image level. Yet progress in the flowering intensity estimation at tree level is much less and still far from satisfactory. To tackle this problem, a novel approach was designed for the use of single raw aerial images to quantify flower intensity. With pre-prepared dataset, flower-associated pixels were extracted for individual trees using a pixel-based classification method, the color thresholding. Next, three flowering indices retrieved from unmanned aerial vehicle (UAV) were evaluated, the index percentage (IPG), index pixel (IP), and index area (IA). Finally, linear correlation of the flowering indices to flower cluster number and expert-assessed floridity recorded in the field were calculated. Results indicated that IPG yielded the highest correlation to flower cluster (R2 = 0.93, RMSE = 8) and floridity estimation (R2 = 0.78, RMSE = 0.9). A UAV-based floridity scoring method was also designed for automatic estimation tasks in practice, and a comparable and even better performance to the expert-based approach was demonstrated. Furthermore, effects of vertical (nadir) and horizontal (angular) overlapping of flower clusters within the canopy were evaluated, showing excellent potential to improve the estimation accuracy